Buch, Englisch, 488 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1960 g
With Applications to Neural Networks
Buch, Englisch, 488 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1960 g
Reihe: Communications and Control Engineering
ISBN: 978-1-85233-373-7
Verlag: Springer
provides a formal mathematical theory addressing intuitive questions of the type:
• How does a machine learn a concept on the basis of examples?
• How can a neural network, after training, correctly predict the outcome of a previously unseen input?
• How much training is required to achieve a given level of accuracy in the prediction?
• How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite time?
The second edition covers new areas including:
• support vector machines;
• fat-shattering dimensions and applications to neural network learning;
• learning with dependent samples generated by a beta-mixing process;
• connections between system identification and learning theory;
• probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithms.
It also contains solutions to some of the open problems posed in the first edition, while adding new open problems.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
- Mathematik | Informatik Mathematik Algebra Algebraische Strukturen, Gruppentheorie
- Mathematik | Informatik EDV | Informatik Technische Informatik Externe Speicher & Peripheriegeräte
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme
- Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
- Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware
- Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
Weitere Infos & Material
1. Introduction.- 2. Preliminaries.- 3. Problem Formulations.- 4. Vapnik-Chervonenkis, Pseudo- and Fat-Shattering Dimensions.- 5. Uniform Convergence of Empirical Means.- 6. Learning Under a Fixed Probability Measure.- 7. Distribution-Free Learning.- 8. Learning Under an Intermediate Family of Probabilities.- 9. Alternate Models of Learning.- 10. Applications to Neural Networks.- 11. Applications to Control Systems.- 12. Some Open Problems.
